2. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 2
Introduction
Simulation and Software Technology, Cologne/Berlin
Head of Intelligent and Distributed Systems department
Institute of Data Science, Jena
Head of Secure Software Engineering group
Co-Founder
Data Scientist
Patient
3. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 3
Motivation – Use Cases
Quantified Self (n = 1 participant) Medical Trials (n > 1 participants)
4. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 4
Motivation – Use Cases
Telemedicine Medical experiments
5. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 5
Understand, how Quantified Self data has been produced, processed,
stored, accessed, …
Pictures from Breakout Session on Mapping Data Access (2014 QS Europe Conference, Amsterdam)
https://forum.quantifiedself.com/t/breakout-mapping-data-access/995
6. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 6
Example: Weight Tracking Workflow
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Questions related to Quantified Self Data and Activities
Data
• What data about the user were created during the activity X?
• What data about the user were automatically generated?
• What data about the user were derived from manual input?
Apps and Services
• Which activities support visualization of the users data?
• In which activities can the user input data?
• What processes are communicating data?
Access and Privacy
• What parties were involved in generating data X?
• What parties got access on data X?
• Can other parties see user’s data X?
8. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 8
Provenance Model for Quantified Self
Sub models for basic Activities
• Input
• Sensing
• Export
• Request
• Aggregate
• Visualize
The activities generate or change data
that is associated or attributed to Agents
• Users
• Software
• Organizations
• Schreiber, A. (2016) A Provenance Model for Quantified
Self Data. In: Universal Access in Human-Computer
Interaction. Methods, Techniques, and Best Practices: 10th
International Conference, UAHCI 2016, Held as Part of HCI
International 2016, Toronto, ON, Canada, July 17-22, 2016,
Proceedings, Part I, Springer, 382-393
• Schreiber A., Seider D. (2016) Towards Provenance
Capturing of Quantified Self Data. In: Provenance and
Annotation of Data and Processes. IPAW 2016. Lecture
Notes in Computer Science, vol 9672. Springer, Cham
9. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 9
UserData
Input
User
wasGeneratedBy
wasAssociatedWith
wasAttributedTo
prov:startTime
prov:endTime
prov:type
prov:type
prov:label
prov:time
Software
type= prov:SoftwareAgent
prov:label
wasAssociatedWith
type=prov:Person
prov:label
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11. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 11
UserData
Visualize
User
Graphic
used
wasGeneratedBy
wasDerivedFrom
type=prov:Person
prov:label
wasAttributedTo
prov:type
prov:label
prov:type
prov:label
prov:time
prov:time
prov:type
wasAttributedTo
Software
type= prov:SoftwareAgent
prov:label
wasAssociatedWith
prov:startTime
prov:endTime
prov:type
12. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 12
13. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 13
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Standard Graph Visualizations and Textual Representations of
Provenance Data are not Easy to Understand by Non-experts
15. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 15
Idea: Provenance Visualization Using Comics
Provenance Comics
• Presenting the provenance of processes in visual representation that people can understand
without prior instructions or training (“Provenance for people”)
• Assumption
• People are familiar with comics from every day life
• See daily strips in newspapers etc.
16. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 16
Provenance Comics
Design considerations
• Data provenance has a temporal aspect: origin, manipulation, transformation,
and other activities happen sequentially over time
• The directed acyclic provenance graph guarantees that, while moving
through its nodes, one always moves linearly forward in time
• It’s possible to derive a temporal sequence of happenings from
the graph that can be narrated like a story
Mapping provenance graph to comics
• We generate a comic strip for each basic activity in the provenance graph
• Each strip consists of a varying number of panels, which are small drawings
that provide further details about the activity
• The complete set of comic strips shows the “story” of the data
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First Sketches
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First Sketches
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Current Graphical Style
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Single Comic Strip Shows a Single Data-related Action
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Communicate to People Where Data is Stored
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Understand How Data is Analyzed
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Distinctive Features
• Shapes
• Colors
• Icons
• Letters
• Labels
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Representation of PROV Elements
Agents
Entities
Activity-related
25. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 25
Collecting QS Provenance
Weight Tracking App
https://play.google.com/store/apps/details?id=de.medando.weightcompanion
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Collecting QS Provenance
Visualization with Python Script
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Open Issues
Current implementation is a prototype with limitations
• Flexibility and generalization
• Handling of
• large provenance graphs
• incomplete provenance data
• branches and multiple data sources
• Expects a single PROV document
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Future Work and Use Cases
Future Work Possible Use Cases
• Different comic styles
• Comparative user studies
• Quantitative comics
• Geographical information
• Glyph-based depiction
• Technical improvements
• Large Provenance graphs
• Provenance templates
• “Intelligent” generation of pictures
• Journalism
• Generation of handbooks
• Communicating incidents
33. > TaPP'17 > Andreas Schreiber, Regina Struminski • Visualizing Provenance using Comics > 23.06.2017DLR.de • Chart 33
Thank You!
Andreas Schreiber
www.DLR.de/sc/ivs
andreas.schreiber@dlr.de
@onyame